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76% of organizations now have a Chief AI Officer - up from 26% just one year ago, according to IBM’s latest CEO study. For many companies, AI is moving from experimentation into execution. They're no longer asking whether AI matters - they're asking who should lead it. IBM’s research shows that many of these leaders come from data, business strategy, innovation, enterprise technology and operations backgrounds. That range is important. When assessing AI transformation candidates, I tend to look for 3 core skillsets: 1️⃣ TECHNICAL FLUENCY Do they understand the architecture, infrastructure and tooling required to make AI work reliably across the enterprise? 2️⃣ STRATEGIC JUDGMENT Can they pick the right problems, align AI investment with business strategy and know where to focus when everything feels possible? 3️⃣ CHANGE LEADERSHIP Will they bring people along, manage resistance, build trust and ultimately get solutions adopted within an org? The weighting across those three areas will vary by company... -> A highly technical organization may need deeper architecture and engineering credibility. -> One with fragmented operations may need someone stronger on influence and execution. -> A company still trying to connect AI to value may need sharper commercial judgment. No Chief AI Officer is built the same. But the best ones usually sit at the intersection of technology, strategy and adoption. AI transformation rarely fails because the tech isn't powerful enough. It fails when companies pick the wrong problems, build without the right foundations or underestimate the human work required to make change stick.

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I love this (adapted) illustration from the recent Linear interview with Chintan Turakhia from Coinbase - it reflects how a lot of leaders I'm speaking to are thinking about deploying AI agents within their operations. In the article, Chintan describes how SWEs at Coinbase are spending significantly less time on writing/reviewing/fixing code - which is now handled predominantly by agents - instead front-and-back-loading their time on planning, designing and ultimately deploying products. This example is specific to software engineering, but the logic holds across other domains: the shape of how people spend their time at work is going to change dramatically as AI is integrated into workflows. Many tasks won't be a simple one-time handoff. In practice, it may be more interspersed - you're going back and forth multiple times, iterating together to complete a project. But in time, every job is going to have its own version of this graph. The shape of these curves is something that every business and its employees need to figure out. AI transformation leaders serve an important function in helping to guide decisions about where handoffs should occur - and importantly, when humans need to take back control.

The AI adoption gap between the US and Europe is growing fast. A new National Bureau of Economic Research working paper just dropped the most comprehensive comparative study to date, surveying ~55,000 workers across seven countries. The headline numbers: → 43% of US workers use genAI for their jobs vs. 32% in Europe → US workers spend 5.2% of work hours on AI vs. 1.5–2.8% in Europe → US adoption grew 3.6% in 8 months... the lowest-adoption European countries barely moved → A 10-pt. increase in adoption correlates with 3-5% of cumulative productivity growth About 55% of the shortfall comes from compositional differences - more college-educated workers, different industry representation, larger firms. But that only tells half the story. The researchers find that company management practices are strongly linked to AI adoption. Countries where firms reward performance, promote on merit, and address poor performers have higher adoption. The US scores highest on this index. Within countries, a one-standard-deviation increase in a worker's management index is associated with a 9.6% jump in adoption. But the single most powerful predictor? Quite simply - whether the employer **actively encourages staff to use AI.** Encouragement alone explains 80% of the average gap. AI training on its own doesn't predict adoption once you control for encouragement and tool provision. It's the active push and access that matter - not the workshops. The bottom line: this isn't a technology availability problem. AI tools cost the same in the US and Europe. The difference is organizational. Leaders that actively encourage adoption, provide tools, and build performance-oriented cultures drive dramatically higher usage - and will ultimately reap the benefits. --- Paper linked in the comments, thanks to the researchers for their work: Alexander Bick, Adam Blandin, David Deming, Nicola Fuchs-Schündeln, Jonas Jessen